code: def __init__(
      self,
      input_dim: int,
      segment_length: int,
      right_context_length: int,
      kernel_size: int,
      activation: str = "silu",
      dropout: float = 0.0,
  ):
      super().__init__()
      self.input_dim = input_dim
      self.segment_length = segment_length
      self.right_context_length = right_context_length
      self.state_size = kernel_size - 1
  
      self.pre_conv = torch.nn.Sequential(
          torch.nn.LayerNorm(input_dim),
          torch.nn.Linear(input_dim, 2 * input_dim, bias=True),
          torch.nn.GLU(),
      )
      self.conv = torch.nn.Conv1d(
          in_channels=input_dim,
          out_channels=input_dim,
          kernel_size=kernel_size,
          stride=1,
          padding=0,
          groups=input_dim,
      )
      self.post_conv = torch.nn.Sequential(
          torch.nn.LayerNorm(input_dim),
          _get_activation_module(activation),
          torch.nn.Linear(input_dim, input_dim, bias=True),
          torch.nn.Dropout(p=dropout),
      )
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models.emformer import _EmformerAttention, _EmformerImpl, _get_weight_init_gains
label_desc: Use image node_id 1 to create a new _ConvolutionModule object from inherited base classes: torch.nn with example: obj = _ConvolutionModule(input_dim, segment_length, right_context_length, kernel_size, activation, dropout)